Data Analytics
- This helps in building essential data capturing architecture for future forecasted and how to do it or achieve it.
- There is a need to be digging for the “what” and “whys” of data capabilities before necessity arises. This process should be followed by tool implementation.
- Data Analytics involves the action part of collecting, predicting and forecasting the data for the future.
- Data Analytics involves decision making of what will be required, how we can gather data, decide the tools and design the requirement to help generate such data which will help in future.
- Data Analytics is the cause, the façade to help future data generation.
Examples: Google Analytics, Adobe Analytics, Webtrends
Business Intelligence
- More about what can be pulled out at the present moment.
- This is more related to which info to pull and create the reports, creating a bigger picture.
- Business Intelligence is how you mold the provided information into something useful for the business.
- Business Intelligence is more about reporting, dashboards and creating datasheets for the business use-case
- Business Intelligence is one of the cause for data analytics.
Examples: Oracle BI, Microsoft BI
Both of these things are relatable but let’s not confuse between them. BI can be counted as a subset of Data Analytics which is a wider term encompassing Data Science, BI and Data Visualization.
